#!/usr/bin/python
# -*- coding: utf-8 -*-

# Copyright (c) 2011
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
#
# Author: Jesus Carrero <j.o.carrero@gmail.com>
# Mountain View, CA
#

import scipy as sc
import numpy as np
import solvers.SorSolvers as Sor

if __name__ == '__main__':
    dim = 10
    b = sc.rand(dim, 1)
    M = sc.rand(dim, dim)
    diag = np.sum(abs(M), axis=0)

    sum_off_diag = np.sum(abs(M), axis=1) - np.abs(np.diag(M))

    for i in np.arange(dim):
        M[i, i] = abs(M[i, i])+sum_off_diag[i]

    omega = 1.5
    const = np.zeros(b.shape)
    sys = Sor.Sor(M, b, 'pSor', omega)

  # sys.set_tolerance(1.0e-14)

    sys.solve()
    solution = sys.solution()
    print solution

